A Review of CNN-Based Techniques for Accurate Plant Disease Detection
Hamed, B., Hussein, M., Mousa, A. (2023). A Review of CNN-Based Techniques for Accurate Plant Disease Detection. EKB Journal Management System, 10(3), 44-51. doi: 10.21608/ijci.2023.235519.1135
Bahaa Samy Hamed; Mahmoud Hussein; Afaf Mousa. "A Review of CNN-Based Techniques for Accurate Plant Disease Detection". EKB Journal Management System, 10, 3, 2023, 44-51. doi: 10.21608/ijci.2023.235519.1135
Hamed, B., Hussein, M., Mousa, A. (2023). 'A Review of CNN-Based Techniques for Accurate Plant Disease Detection', EKB Journal Management System, 10(3), pp. 44-51. doi: 10.21608/ijci.2023.235519.1135
Hamed, B., Hussein, M., Mousa, A. A Review of CNN-Based Techniques for Accurate Plant Disease Detection. EKB Journal Management System, 2023; 10(3): 44-51. doi: 10.21608/ijci.2023.235519.1135

A Review of CNN-Based Techniques for Accurate Plant Disease Detection

IJCI. International Journal of Computers and Information
Article 7, Volume 10, Issue 3, November 2023, Page 44-51  XML PDF (304.36 K)
Document Type: Original Article
DOI: 10.21608/ijci.2023.235519.1135
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Authors
Bahaa Samy Hamed email 1; Mahmoud Husseinorcid 2; Afaf Mousa3
1Computer science , faculty of computers and information, Menoufia University , Alexanderia
2Computer Science Department, Faculty of Computers and Information, Menoufia University
3computer science , faculty of computers and information, menoufia university
Abstract
Abstract— Various techniques have revolutionized the field of plant disease detection, offering accurate approaches for timely detection and recognition of crop diseases. This comprehensive review explores the current utilization of diverse techniques for plant disease detection and classification. It analyzes recent publications, considering aspects such as disease detection methods and dataset characteristics. These techniques have significantly advanced object detection and recognition in agriculture, facilitating efficient crop management and higher yields. However, the complexity of identifying and detecting plant diseases from images necessitates species-specific detection for customized control strategies. This study discusses the challenges and proposed solutions associated with the use of different techniques in early disease detection concentrated on deep learning methods. Overall, the review demonstrates the considerable potential of these techniques in disease detection and emphasizes the ongoing need for research and development to address current challenges and optimize their benefits in agriculture. and also underscores the importance of incorporating emerging technologies and data-driven approaches to further enhance the precision and scalability of plant disease detection systems.
Keywords
Deep learning; CNN; plant disease datasets; pre-trained models
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